Spectral imaging of biofilms

ABSTRACT

A spectroscopic method and system to identify a biofilm of a microorganism. A sample containing a sample microorganism is irradiated with substantially monochromatic radiation. A Raman data set is obtained based on radiation scattered from the irradiated sample. A database is searched in accordance with the Raman data set in order to identify a known Raman data set from the database. The database contains a plurality of known Raman data sets where each known Raman data set is associated with a known sessile form of a corresponding known microorganism. A sessile form of the sample microorganism is identified based on the known Raman data set identified by the searching.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.11/598,245 filed Nov. 9, 2006, now U.S. Pat. No. 7,450,228, which claimspriority to U.S. Provisional Patent Application No. 60/734,839 filedNov. 9, 2005, each of which are incorporated herein by reference intheir entirety.

FIELD OF THE DISCLOSURE

This application generally relates to the identification of biofilms byRaman spectroscopy.

BACKGROUND OF THE DISCLOSURE

Microorganisms sometimes grow in films or mats, in which not allmicroorganisms in the film are identical. The form, function,composition, and metabolic state of cells can vary depending on theirlocation in the film. Cells on the exterior surface of the film canexhibit different properties and functions than cells in the interior ofthe film or cells at the surface of the film that is in contact with asubstrate.

Biofilms are usually composed of both an extracellular matrix and cells.The matrix can anchor the cells to the substrate and provide a matrix inwhich the cells can live. Common biofilm materials include extracellularpolysaccharides secreted by bacteria. Proteins exhibiting specific ornon-specific binding properties can form biofilms, with or withoutcells. By way of example, bacteria having a cell-surface receptor orpilus protein having a specific binding affinity for a substrate canbind to that substrate and adhere the bacteria to the substrate. Furtherby way of example, proteins in a fluid system can aggregate at a surfaceand aggregation of protein at the surface can provide a substratesuitable for further aggregation of the same or different proteins, evenin the absence of cells.

The term ‘biofilms’ is used in a variety of ways in the literature, Mostoften, it refers to a colonial structure composed of microorganism cellsand extracellular matrix. However, the term is sometimes used to referto conglomerations of biological molecules smaller than cells (e.g.antibodies, blood proteins, or lipoprotein complexes that form a film onsurfaces that contact blood). In some medical contexts, the term is alsoused to refer to cells of an animal that adhere to a surface in theanimal (i.e., not cells which infect the animal, but the animal's owncells).

When cells are present in a biofilm, they are not necessarily all of thesame species or type. Most naturally-occurring biofilms contain morethan one type of organism, some of which actively generate matrixmaterials and others of which merely become trapped or adhered in thematrix. Organisms in a biofilm are known to be able to cooperate to forma community. Other times, the matrix is formed substantially only by oneorganism and the other organism(s) merely colonize the matrix, takingadvantage of its presence, but not really contributing to itsconstruction or maintenance.

In at least some biofilms, communication, coordination, or both appearsto occur between cells in the films. Such communication appears to bemediated by chemical compounds released by individual cells and ‘sensed’in some manner by other cells. Biofilms tend to be more difficult to getrid of than individual microorganisms or non-film colonies ofmicroorganisms. Such resilience to biocides likely stems from somecombination of resistance of the film to penetration of the biocide(shielding interior cells), persistence in the film of biocide-resistantforms of microorganisms (e.g., spores) that can survive the biocide andregenerate after the biocide is removed, and ability of biofilm thathappens to survive biocide treatment to ‘shed’ bits of itself andre-seed the system in which it exists.

Others have recognized that biofilms can be examined using Ramanspectroscopy. However, it is believed that this is the first descriptionof differentiation of certain characteristics of biofilms that can beachieved using Raman chemical imaging and related techniques.

SUMMARY OF THE DISCLOSURE

The present disclosure provides for a method to identify a biofilm of amicroorganism. A sample containing a sample microorganism is irradiatedwith substantially monochromatic radiation. A Raman data set is obtainedbased on radiation scattered from the irradiated sample. A database issearched in accordance with the Raman data set in order to identify afirst known Raman data set from the database. The database contains aplurality of first known Raman data sets where each first known Ramandata set is associated with a known sessile form of a correspondingknown microorganism. A sessile form of the sample microorganism isidentified based on the first known Raman data set identified by thesearching.

In one embodiment, the database further includes a plurality of secondknown Raman data sets. Each second known Raman data set is associatedwith a known planktonic form of a corresponding known microorganism, andeach second known Raman data set has a corresponding first known Ramandata set. The database is farther searched in accordance with the Ramandata set in order to identify a second known Raman data set from thedatabase. A planktonic form of the sample microorganism is identifiedbased on the second known Raman data set identified by the furthersearching.

In one embodiment, the sample microorganism includes extracellularmaterial associated with the sessile form of the sample microorganism.In another embodiment, the sample microorganism includes a celltransmitter.

In yet another embodiment, the sample includes cells associated with thesessile form of the sample microorganism. The cells may be treated witha compound and then irradiated with substantially monochromaticradiation. A second Raman data set produced by the irradiated treatedsample. The Raman data set is compared with the second Raman data set todetermine the effect of the compound on the cells associated with thesessile form of the sample microorganism. In one embodiment, thecompound includes an antibiotic.

In another embodiment the sample includes a tissue sample of an animal.The animal may include a human having a localized pathogenic conditionsuch as an ear infection. In yet another embodiment, the animal mayinclude a human having a pathogenic condition. In still anotherembodiment, the sample may include a surface site.

The present disclosure further provides for a system including amonochromatic illumination source, a spectroscopic device, an imagingdevice, a database and a machine readable program code containingexecutable program instructions and a processor. The processor isoperatively coupled to the monochromatic illumination source, thespectroscopic device and the imaging device and is configured to executethe machine readable program code to perform a series of steps. Thedatabase contains a plurality of known Raman data sets where each knownRaman data set is associated with a known sessile form of acorresponding known microorganism.

BRIEF SUMMARY OF THE SEVERAL VIEWS OF THE DRAWINGS

The accompanying drawings, which are included to provide furtherunderstanding of the disclosure and are incorporated in and constitute apart of this specification, illustrate embodiments of the disclosureand, together with the description, serve to explain the principles ofthe disclosure.

FIG. 1 is an exemplary system used to carry out the methods of thepresent disclosure.

FIG. 2, includes FIGS. 2A, 2 B, 2 C, and 2 D. FIGS. 2A, 2 B, and 2 C arerepresentative captured video images showing bright-field reflectance ofplanktonic Pseudomonas at a magnification of 100× for three dilutions.FIG. 2D is a plot of three dispersive Raman spectra of the samples shownin FIGS. 2A, 2 B, and 2 C. In FIG. 4D, the spectra were truncated, whitelight-corrected, and normalized.

FIG. 3 is a comparison of the Raman spectrum 310 of the planktonicPseudomonas shown in FIG. 2 and of a spectral library spectrum 320 forPseudomonas aeruginosa.

FIG. 4 is a comparison of interpolated dispersive spectra of planktonic410 and biofilm 420 forms of P. aeruginosa. The inset highlights a peakshift.

FIG. 5, including FIGS. 5A, 5B, and 5 C, is a Raman chemical image ofplanktonic P. aeruginosa taken at 2920 cm⁻¹ and a magnification of 100×(FIG. 5A) and of two regions of the Raman spectra (FIGS. 5B and 5C) ofthe portions of FIG. 5A.

FIG. 6 is a bright-field reflectance image of planktonic P. aeruginosaoverlaid with the Raman chemical information shown in FIG. 5. Area 610indicates Raman scattering at 890 cm⁻¹ and area 620 indicates Ramanscattering at 2920 cm⁻¹.

FIG. 7, including FIGS. 7A and 7B, is a Kaman chemical image ofbiofilm-associated P. aeruginosa cells taken at 1440 cm⁻¹ (FIG. 7A) andof the Raman spectra (FIG. 7B) for the corresponding regions ofinterest.

FIG. 8, including FIGS. 8A and 8B, is a Raman chemical image of the sameframe of biofilm-associated P. aeruginosa matrix shown in FIG. 7, takenat 1440 cm⁻¹ (FIG. 8A) and of the Raman spectra (FIG. 8B) for thecorresponding regions of interest.

FIG. 9 is a bright-field reflectance image of the same frame ofbiofilm-associated P. aeruginosa shown in FIGS. 7 and 8, overlaid withthe Raman chemical information shown in FIGS. 7 and 8. Area 910indicates Raman scattering at 800 cm⁻¹ and area 920 indicates Ramanscattering at 1580 cm⁻¹.

DETAILED DESCRIPTION

The disclosure relates generally to the observation that cells anchoredto or embedded in a biofilm (i.e., sessile cells) can be distinguishedspectroscopically from planktonic (i.e., free-living) cells of the samespecies. The disclosure further relates to the observation that themethods described herein can also be used to distinguish cellsassociated with a biofilm from one another, for example by function orby metabolic state.

DEFINITIONS

As used herein, each of the following terms has the meaning associatedwith it in this section.

A “sessile” cell is a cell which is associated (i.e., by contact,adhesion, anchoring, immersion, or otherwise) with a biofilm or with asignificant piece (i.e., a piece larger than the cell) of biofilm matrixthat has been shed from a biofilm.

A “planktonic” cell is a cell which exists substantially withoutcell-to-cell adhesion (beyond that normally associated with celldivision) and which exists substantially independent of extracellularmaterials holding multiple cells in association with one another.

DESCRIPTION

It has been discovered that Raman chemical imaging and similarspectroscopic methods can be used to distinguish planktonic cells fromsessile cells of the same species. Surprisingly, the spectra of thesetwo types of cells appear to differ in ways not directly attributable tothe presence of acellular biofilm matrix material. For example, asdescribed herein, the Raman spectrum of planktonic Pseudomonasaeruginosa cells appears to differ in subtle, but distinguishable waysfrom P. aeruginosa. These discoveries indicate that Raman chemicalimaging and related techniques can be used for a variety of purposesdescribed herein.

Examples of other imaging techniques that can be used in the devices andmethods described herein include fluorescence, absorption, polarizationimaging techniques. Use of such imaging techniques (in addition to or inconjunction with Raman imaging techniques) can generate data which canbe processed to yield intermediate images which can be useful in thestudy of biofilms. For example, acquisition of a series of images ofRaman scattered light at a plurality of Raman shift values yields aspatially accurate, wavelength-resolved dataset that can be manipulatedusing techniques such as principal component analysis (PCA),multivariate curve resolution (MCR), cosine correlation analysis (CCA),Euclidian distance analysis (EDA), partial least squares regression(PLSR), or spectral mixture resolution (SMR) to yield a molecular imagewhich highlights molecular distinctions within the field of view orscene which is originally imaged. These techniques can also be appliedto fluorescence and absorption image datasets. Image datasets obtainedby multiple techniques can be combined to yield hybrid images (or otherdata representations) using known data-handling techniques.

For instance, the methods and devices described herein can be used toidentify the presence or absence of sessile forms of a microorganism,such as in settings in which it is important to detect biofilm formationat an early time—before heavy accumulation of biofilm material canoccur. The methods and devices described herein can also be used todetermine the effect of a composition (e.g., an antibiotic) on cells ofa biofilm. Such analysis facilitates development of effectivecompositions for countering accumulation and survival of biofilms.

Biofilms can develop at substantially any fluid interface and likely atnon-fluid interfaces as well, such as the surface of mineral particles.The medical relevance of biofilms has been widely reported in theliterature. Biofilms can directly contribute to pathology, such as withfilms of infectious organisms that can develop in ear infections(particularly in recurrent ear infections). Biofilms can also contributeindirectly to pathology in animals, such as when a biofilm which residesin tissue of one part of an animal body (at which the biofilm does notcause adverse effects) sheds portions of itself into another part (e.g.,the bloodstream) of the animal, leading to a persistent infection thatdoes not respond adequately to antibiotic treatment. Fouling of chemicalprocessing equipment, water treatment equipment, ship hulls, and otherliquid-contacting components is a common problem. Anti-fouling researchhas been stymied by inadequate methods of assessing biofilms and theircomponents.

Owing to the widespread occurrence of biofilms, the methods and devicesdescribed herein have widespread applicability. They can be used todetect and distinguish biofilms, their component organisms, theircomponent acellular components, and the functionally- ormetabolically-different parts of a biofilm.

Spectroscopic Distinction of Sessile and Planktonic Microorganisms

Biofilms formed by or of microorganisms (eubacteria, archaebacteria, andeukaryotes such as yeasts and molds) are among those most commonlyencountered. Many of these microorganisms can exist in a free-living,planktonic form and in a relatively geographically-fixed, sessile form.It has been established by others that there can be significantmetabolic, morphological, and functional differences between planktonicand sessile forms of the same organism. However, in view of thedifficulty of sample collection and analysis which others haveexperienced, it is generally regarded as difficult to distinguishplanktonic and sessile forms of an organism outside of carefullycontrolled laboratory conditions.

The subject matter described herein includes methods and devices foridentifying the occurrence of the planktonic and sessile forms of amicroorganism in a sample. The methods can be performednon-destructively and at substantially any site at which light scatteredby the cells can be gathered. Crudely summarized, the method comprisesassessing radiation (e.g. light, whether visible or not) scattered froma sample. That scattered radiation is compared with a known scatteringproperty of a sessile or planktonic form of the microorganism. Detectionof the known scattering property is an indication that the correspondingsessile or planktonic form of the microorganism occurs in the sample.

FIG. 1 schematically represents an exemplary system 100 used to performthe methods of the present disclosure. System 100 includes, in a singleplatform, an imaging device in the form of a microscope objective 106, aspectroscopic device in the form of an imaging spectrometer 117 or adispersive spectrometer 121, a processor 127, a database 125, amicroscope stage 103, machine readable programmable code 129 and a probe130. System 100 further includes a monochromatic light source 107, whitelight source 105, and bandpass filter 109 which removes SiO₂ bandsarising from a laser excitation fiber optic. The laser light is directedto a band reject optical filter 110 and propagated through an imagingobjective 106 to illuminate the sample 101 with substantiallymonochromatic light. Objective 106 functions to collect photonsemanating from sample 101 which result in a Raman data set. Notchfilters 112 and 113 reject light at the laser wavelength. In oneembodiment, sample 101 contains a sample microorganism. Based onradiation scattered from the irradiated sample, a Raman data set isobtained. Dispersive spectrometer 121 functions to separate thescattered photons into a Raman spectrum characteristic of the samplemicroorganism. Imaging spectrometer 117 functions to filter thescattered photons into one or more spatially accurate wavelengthresolved images characteristic of the sample microorganism. The Ramandata set may also include data subsets characteristic of one or more ofthe following: extracellular material, a cell associated with thesessile form of the sample microorganism and a cellular transmitter.

The machine readable program code 129 contains executable programinstructions. Processor 127 is operatively coupled to the illuminationsource 107, spectroscopic device 117 or 121 and imaging device 106.Processor 127 executes the machine readable program code 129 toconfigure the illumination source 107 to irradiate the sample 101 withsubstantially monochromatic light. Processor 127 also executes themachine readable programmable code 129 to configure the spectroscopicdevice 119 or 121 to obtain a Raman data set based on the scatteredradiation produced by the sample 101. Processor 127 is also configuredto execute the machine readable program code 129 so as to perform themethods of the present disclosure.

Processor 127 is configured to execute a machine readable program code129 which functions to search the database 125. For the Raman data setor data subset, the database can be searched using a variety ofsimilarity metrics. The metrics include Euclidean Distance, the SpectralAngle Mapper (SAM), the Spectral Information Divergence (SID),Mahalanobis distance metric and spectral unmixing. A spectral unmixingmetric is disclosed in U.S. Pat. No. 7,072,770 B1 entitled “Method forIdentifying Components of a Mixture via Spectral Analysis,” which isincorporated herein by reference in its entirety.

The database 125, of system 100, contains two pluralities of known Ramandata sets for known microorganism. Each of the first known Raman dataset is associated with a known sessile form for corresponding knownmicroorganism. Each of the second known Raman data set is associatedwith a known planktonic form of a corresponding known microorganism.Each second known Raman data set also has a corresponding first knownRaman data set. In one embodiment, the first known Raman data setincludes a plurality of Raman spectra corresponding to the known sessileforms of the respective known microorganisms. In a second embodiment,the first known Raman data set includes a plurality of spatiallyaccurate wavelength resolved Raman spectroscopic images corresponding tothe known sessile forms of the respective known microorganisms. Inanother embodiment, the second known Raman data set includes a pluralityof Raman spectra corresponding to the known planktonic forms of therespective known microorganisms. In yet another embodiment, the secondknown Raman dataset includes a plurality of spatially accuratewavelength resolved Raman spectroscopic images corresponding to theknown planktonic forms of the respective known microorganisms.

In one embodiment, database 125 further includes a plurality of thirdknown Raman data sets wherein each third known Raman data set isassociated with a known extracellular material. In another embodiment,database 125 includes a plurality of fourth known Raman data sets,wherein each fourth known Raman data set is associated with a known cellhaving an associated function. In yet another embodiment, database 125includes a plurality of fifth known Raman data sets, wherein each fifthknown Raman data set is associated with a known cell transmitter.

The machine readable program code 129 also functions to identify thesample based on the searching results. As discussed above, the searchingresults are obtained by searching the database 125 using a similaritymetric. In one embodiment, the similarity metric searches the database125 in accordance with a Raman data set to identify a first known Ramandata set associated with a known sessile form of a known microorganism.In one embodiment, the searching results generates a match between theRaman data set of the sample microorganism and a first known Raman dataset associated with a known sessile form of a known microorganism. Thesessile form of the sample microorganism is identified based on thesearching results. In another embodiment, the similarity metric searchesthe database 125 in accordance with a Raman data set to identify asecond known Raman data set associated with a known planktonic form of aknown microorganism. In one embodiment, the searching results generatesa match between the Raman data set of the sample microorganism and asecond known Raman data set associated with a known sessile form of aknown microorganism. The planktonic form of the sample microorganism isidentified based on the searching results. The identification of thesessile and planktonic forms of the sample microorganism is based onRaman spectroscopic data for known microorganisms that is discussedbelow.

The machine readable program code 129 also functions search database 125and to identify extracellular material and cells associated with thesessile form of the unknown sample microorganism and cellulartransmitter associated with the sample microorganism, based on thesearching results. In another embodiment, the similarity metric searchesthe database 125 using a data subset to identify a third known Ramandata set. The data subset may be associated with extracellular materialassociated with the sessile form of the sample microorganism. In stillanother embodiment, the similarity metric searches the database 125using a data subset to identify a fourth known Raman data set. The datasubset may be associated with cells associated with the sessile form ofthe sample microorganism. In yet another embodiment, the similaritymetric searches the database 125 using a data subset to identify a fifthknown Raman data set. The data subset may be associated with cellulartransmitters.

Though the discussion herein focuses on the system illustrated in FIG.1, the practice of the method of this disclosure is not limited to sucha system. An alternative system with the ability to deliver digitalimages and spectroscopic data sets is described in U.S. Pat. No.7,046,359 entitled “System and Method for Dynamic Chemical Imaging”which is incorporated herein by reference in its entirety.

In a preferred embodiment, a Raman scattering property of themicroorganism is examined. Use of Raman-shifted scattered lightdecreases interference with light that is reflected or elasticallyscattered by the sample, permitting more detailed analysis of the samplethan might otherwise be possible. In order to assess Raman-shiftedscattered light, a monochromatic light source should be used toilluminate the sample and a filter that substantially preventstransmission of light having the same wavelength as the light sourceshould be interposed between the sample and the Raman detector. Ramanspectroscopy is a preferred method because the Raman spectrum of asample can be used to identify a wide variety of materials andorganisms, each of which can exhibit characteristic Raman spectralproperties that facilitate their identification.

Light scattered from the sample can be identified in bulk (e.g., using adispersive spectrometer) or at a plurality of regions on or within thesample. Scattering data can be combined with other information generatedby spectroscopic or imaging methods. By way of example, assessment ofRaman scattering at multiple sites in a microscopic field can becombined with a visible light reflectance image of material within thefield. The result in this example can be a visible light image of thebiofilm in a microscopic field of view having portions highlighted,corresponding to portions at which a component of sessile microorganismsis detected by Raman spectroscopy. When multiple imaging techniques areemployed, the resolution of the two techniques need not be the same. Byway of example, a visible light reflectance image which depicts amicroorganism as an area of approximately 100 picture elements (pixels)can be combined with a Raman chemical image which depicts the samemicroorganism as an area of approximately 10 pixels or vice versa.

As an alternative to detecting a scattering property of a cell in abiofilm, the methods can also be employed in an embodiment in which ascattering property of the biofilm matrix (i.e., extracellular oracellular materials) is examined. By way of example, the Raman-shiftedscattered radiation occurring at a wavelength characteristic of theextracellular material can be used to indicate the presence of sessile,matrix-producing microorganisms rather than (or in addition to)planktonic cells of the same type. Substantially any extracellularmaterial (e.g. extracellular polysaccharide, pilus protein, orcell-surface binding protein) that can be detected using the scatteringspectroscopic method can be the subject of the analysis.

Distinguishing Sessile and Planktonic Microorganisms

In some situations, the mere presence of a microorganism is lessrelevant than whether the microorganism is forming (or in the form of) abiofilm. The methods and devices described herein can be used todistinguish between planktonic forms of the microorganism at (oradsorbed to) a particular site and sessile forms of the microorganismthat are in or forming a biofilm at the site.

In this embodiment, radiation scattered from a site is collected andcompared with known scattering properties of one or both of sessile andplanktonic forms of the microorganism. Detection of the mown scatteringproperties indicates occurrence in the sample of the correspondingsessile or planktonic form of the microorganism. If occurrence of only asingle microorganism is likely or possible, than no further analysis maybe necessary. If microorganisms of another species may be present at thesite, then the scattering data can be analyzed for characteristics ofboth the microorganism of interest and its sessile or planktonic form.Thus, the methods and devices described herein can be used in sampleswith a single microorganism or in samples in which a variety oforganisms do or may exist.

Whether a microorganism occurs in a planktonic or sessile form can haveimportant implications, especially in medical contexts. Many parts ofthe body of humans and other animals are normally aseptic. Occurrence ofa microorganism in such parts (in any form) is usually a sign ofdisease. At other parts of the body (e.g., skin and vaginal surfaces andmuch of the lining of the digestive tract), a variety of microorganismsnormally occur when the animal is in a healthy state. Ability todistinguish planktonic and sessile forms of a microorganism at a bodylocation can enable one to distinguish pathologic and non-pathologicstates in a tissue sample. Even in normally sterile tissues, theplanktonic/sessile forms of a pathogenic microorganism can influence themost desirable form of medical intervention. The methods and devicesdescribed herein can be used for such purposes by facilitating bothidentification of microorganisms that occur at a body site and theplanktonic/sessile form of the organisms detected there.

For example, the methods and devices described herein can be used toidentify, and to what extent, a microorganism contributes to a localizedpathogenic condition in an animal. In this method, radiation scatteredfrom the locality (i.e., the body location) of the condition iscollected and compared with known scattering properties of a sessileand/or planktonic form of the microorganism. Detection of the knownscattering properties can both identify the organism and indicatewhether it is present in a planktonic or sessile form. This informationcan be used by a medical professional to assess contribution of themicroorganism to the condition. By way of example, detection ofplanktonic forms of a microorganism in a human middle ear infection canindicate that a single, relatively brief round of antibiotic treatmentcan be expected to alleviate the infection. By contrast, detection of asessile form of the microorganism is indicative that the microorganismis present in the form of a biofilm which can be more refractory toantibiotic treatment and which can harbor multiple pathogens,potentially indicating use of stronger, more prolonged antibiotictherapy.

As an example, several bacteria capable of existing in both planktonicand sessile forms have been isolated by others from needle aspirates ofmiddle-ear effusions obtained from patients afflicted with acute otitismedia (AOM). Streptococcus pneumoniae occurs in about 40% of AOMpatients, Haemophilus influenzae occurs in about 25% of AOM patients(especially in younger children), and Marxella cattarhalis occurs inabout 10% of AOM patients. Many bacterial isolates prove to be resistantto amoxicillin. Antibiotic resistance and recalcitrance are observed inclinical practice as well. The mechanism(s) contributing to resistanceand recalcitrance cannot always be discerned in a clinically relevanttime period. The methods described herein can be used to identify theplanktonic an or sessile nature of infectious organisms, in vitro or invivo, and that information can be used to guide treatment decisions.

The methods and devices described herein can be used to identify thecontribution of a microorganism to a non-localized pathogenic conditionin an animal. In this method, radiation scattered from a known potentialbodily reservoir for the microorganism is assessed and the scatteredradiation compared with a known scattering property of a sessile form ofthe microorganism. Detection of the known scattering property issuggestive that a sessile form of the microorganism (one possibly remotefrom the body location at which symptoms are exhibited) contributes tothe condition. In such methods, detection of the microorganism at a bodylocation at which the pathogenic condition is manifested (in either aplanktonic form or in the form of agglomerates of sessile cells ‘shed’from the reservoir) can further confirm the role of the microorganism inthe observed pathology.

Development of Antibiotics and Anti-Fouling Agents

In many settings (e.g., chemical- and water-processing equipment, boathulls, and on tooth enamel), it is well known that biofilms containingmicroorganisms routinely occur. Various agents have been developed toremove or retard development of such biofilms. These development effortshave been hampered by the substantial inability of others to accuratelyassess the effect of the agent on a scale other than a macroscopic scale(e.g., by observing whether the agent eliminated or inhibited slimebuildup). The methods and devices described herein can be used to assessthe effects of an agent on biofilm formation and composition—both on amicroscopic scale and in real time, if desired.

An existing or forming biofilm can be identified as described herein.The biofilm can be contacted with a selected concentration of ananti-biofilm agent (e.g., an antibiotic) for a selected time, and theeffect of such contact on the existence, viability, and function ofcells in a biofilm (or on non-cellular biofilm components, if they areanalyzed as described herein) can be assessed. By way of example, thebehavior of a simulated biofilm-associated biological infection (e.g.,an ear infection) can be observed in the presence of various antibioticsand combinations of antibiotics in order to assess the resilience of thebiofilm to a proposed or actual animal treatment.

Similar methods can be used to identify the development at a site of abiofilm. In such methods, the spectral properties of a substrate areassessed over time, e.g., by intermittently removing and assessing thesubstrate, or by continuously or intermittently assessing it in situ.The sites include such surfaces as pipes, tubing, boat hulls, boatdocks, tooth enamel, intestinal mucosa, medical equipment such ascatheters or dialysis equipment, and liquid holding tanks in chemical orwater treatment plants.

Devices for Assessing Biofilms

Substantially any device capable of collecting and detecting lightscattered by a biofilm can be used to assess the biofilm in the methodsdescribed herein. A device capable of assessing Raman scattering ispreferred. For example, a Raman spectrometer or a Raman chemical imagingmicroscope such as the FALCON® (Chemlmage Corporation, Pittsburgh Pa.)device described herein can be employed. An exemplary device isillustrated in FIG. 1.

Biofilms can be assessed directly or using a probe 130 (e.g., insertedinto an animal body or snaked through a pipe) operably connected with asuitable detector. For example, a fiber optic probe having a monochromicillumination source and optical conduits for transmitting scatteredlights at or near the tip of the probe 130 can be used. A suitable probe130 is described in U.S. Pat. No. 6,788,860 entitled “Chemical ImagingFiberscope” which is incorporated herein by reference in its entirety.

In one embodiment, development of a biofilm over time is studied in asystem in which biofilm formation is common or expected. In thisembodiment, a substrate is included in the system such that thesubstrate can be easily assessed using the methods described herein. Byway of example, the substrate may be a replaceable substrate that can beremoved to an analytical lab. In another embodiment, the substrate is aresident in situ substrate such as a window installed along afluid-contacting surface, such that biofilm formation on the fluid faceof the window can be assessed using a device placed against thenon-fluid face of the window.

Assessing Communication and Diversity within a Biofilm

Others have disclosed that cells within a biofilm can exhibit differentmetabolic and functional characteristics, depending on their locationwithin the biofilm. Others have also described chemical compounds bymeans of which cells in a biofilm appear to communicate with oneanother. Each of these phenomena can be assessed using the methods anddevices described herein.

As set forth in the example, cells in a biofilm can be identified andtend to exhibit one of a discrete but multiple number of Ramanscattering spectra, suggesting that cells of discrete phenotypic typesoccur. The methods described herein can be used to distinguish thosetypes, to enumerate the cells of each type, and to guide separation,study, or ablation of cells of one or more types.

The methods and devices described herein can also be used to detect andquantify cell transmitters in a biofilm. Raman spectroscopy ofindividual compounds is known in the art, as are the chemical identityof many actual and suspected cell communication-mediating compounds(“cell transmitters”). Using traditional Raman chemical imagingtechniques, Raman scattering characteristics of a cell transmitter canbe determined with minimal experimentation, and those characteristicscan be used to assess occurrence and concentration of the celltransmitter in a biofilm. The role of the cell transmitter incommunication among cells in a biofilm can thereby be identified.

Examples

The subject matter is now described with reference to the followingExamples. These Examples are provided for the purpose of illustrationonly, and the subject matter is not limited to these Examples, butrather encompasses all variations which are evident as a result of theteaching provided herein.

Imaging of Planktonic and Sessile Pseudomonas aeruginosa Cells

The experiments described in this Example involved imaging of theplanktonic and sessile (biofilm) forms of Pseudomonas aeruginosa. Theplanktonic and sessile organisms were deposited on aluminum-coatedslides and prepared the samples for imaging.

Planktonic cultures were filtered and then printed to a coupon directlyand in dilutions of 1:10 and 1:100 (corresponding to FIGS. 2A, 2 B, and2 C, respectively). Biofilm cultures were directly gown on slides andair-dried. The samples were characterized by dispersive Ramanspectroscopy. Multiple spectra were taken of all forms and dilutions.After enhancing the spectral signal and obtaining a spectral profile forthe organism, the mean spectrum was compared against Raman spectra ofknown sample microorganisms, in a database, for identification purposes.

Raman chemical imaging was used to differentiate the elements of thesample. Absolute differences between the organisms, the matrix, and thebackground were determined. The imaging runs were obtained using aChemImage Falcon® (ChemImage Corporation) Raman chemical imaging deviceusing a slit width of 50 micrometers, a laser power of 150 to 200milliwatts (with a 532 nanometer green laser), and a grating set at 300grooves per millimeter.

Dispersive Raman Spectroscopy

Dispersive spectra of planktonic form were obtained at eachconcentration and characterized. The spectral profile at each dilutionremained generally uniform, although certain aberrations were observed,as shown in FIG. 2. The fingerprint region of the mean of the spectrashown in FIG. 2D (i.e., spectra of the dilutions of the planktonicorganism) was compared against Raman spectra of known samplemicroorganisms, in a database, and this comparison correctly identifiedthe spectrum as that of Pseudomonas aeruginosa, as is evident from thecomparison of fingerprint regions shown in FIG. 3.

Dispersive spectra of the biofilm form were similar but not identical tothose of the planktonic form, as shown in FIG. 4. Differences inintensities and peak shifts (e.g., see the inset of FIG. 4) within thefingerprint region (here 900 to 1700 cm⁻¹) indicate macromoleculardifferences between the two forms of the organism. In the image shown inFIG. 4, after processing by white light division, base lining, andnormalization, the means were interpolated onto one graph for thepurposes of general comparison. Intensity differences can be observedthroughout the fingerprint region, and peak shifts are present at boththe C-H stretching peak and select peaks in the fingerprint region.

The spectra taken from the biofilm form did not match the referencePseudomonas aeruginosa spectrum in the database as the database does notinclude the Raman spectrum of a biofilm form of the species.

Raman Chemical Imaging

Spatially accurate wavelength resolved images were also used tocharacterize the planktonic and biofilm forms of Pseudomonas aeruginosa,including the media or matrix, if present. Imaging of the planktonicform resolved basic issues, such as capabilities of distinguishingmucosal colonies from the aluminum background. Although it cannot bedetermined whether mucus is obscuring them, individual planktonicorganisms were not resolvable in the images shown in FIG. 5A.

The image in FIG. 5A was taken using the Falcon® device at 100×magnification, at wavenumbers from 800 to 3100 cm⁻¹ at a laser power of190 milliwatts. Three regions of interest 510, 520, and 530 arehighlighted within the field of view shown in FIG. 5A. The two mucosalcolony areas 510 and 530 and the aluminum background area 520 exhibitdifferent spectral profiles. FIGS. 5B and 5C show the correspondingRaman spectra 530 b and 530 c for region of interest 530, Raman spectra520 b and 520 c for region of interest 520 and Raman spectra 530 b and530 c for region of interest 530. The information from the RamanChemical Image was overlaid upon the bright field reflectance videocapture image taken very close to the same field of view. Theinformation from this overlay (shown in FIG. 6) confirmed that thechemical information signal originated from the mucosal colonies andincluded minimal interference from the aluminum background.

The biofilm sample included biofilm-associated (and dissociated) cells,matrix, and media elements, together with the aluminum background. TheRaman chemical images (FIGS. 7 and 8) taken from these samplesdemonstrated spectral characteristics of both the cells and the matrix.The Raman chemical images of FIGS. 7 and 8 were made using the Falcon®device at 100× magnification, from wavenumbers 800 to 1700 cm⁻¹ at alaser power of 200 milliwatts. FIG. 7A shows regions of interest 710,720, 730, 740, 750 and FIG. 7B shows the corresponding Raman spectra 710b, 720 b, 730 b, 740 b and 750 b. FIG. 8A shows regions of interest 810,820, 830 and 840 and FIG. 8B shows the corresponding Raman spectra 810b, 820 b, 830 b and 840 b.

The chemical information obtained from FIGS. 7 and 8 regarding thematrix was processed and superimposed over a bright field video image ofthe same region to ascertain which objects in the field of view had beenresponsible for the signal (see FIG. 9). Area 910 indicates Ramanscattering at 800 cm⁻¹ and area 920 indicates Raman scattering at 1580cm⁻¹.

Comparing the aligned Raman chemical image and the video captured brightfield image, the strongest signals were shown to represent cellularmaterial.

The dispersive Raman spectra of the various concentrations of planktonicPseudomonas show that the Raman signal is consistent in shape. This issupported also by the spectral library search, which matched thisacquisition of a Pseudomonas spectrum to a spectrum taken separately.Reproducibility—between locations, equipment, and sample sources—isdemonstrated by the data shown herein. The library search determined thegenus of the organism but also the species, ranking Pseudomonasaeruginosa above P. putida.

Raman chemical imaging was needed to discern the matrix signal from thatof P. aeruginosa. Interference from the matrix made library comparisondifficult; this could be remedied by addition of a biofilm entry to thespectral library. Comparison of the two dispersive spectra (biofilm andplanktonic) showed a few marked differences that can be used to resolvemolecular differences between these two living forms of the sameorganism. Raman chemical imaging of the biofilm sample provided a clearcontrast between the organisms and the matrix. Although the matrix isprimarily composed of organic and biological molecules such asoligosaccharides, there is a clear chemical imaging contrast betweenmatrix elements and cellular elements. This is true despite the apparentcovering of the cells by the matrix, although the spectra associatedwith the imaged cells do seem to be affected by the enveloping matrixmaterials, The alignment of the bright-field reflectance and the Ramanchemical image confirm that the chemical-differences observed arecorrect representations of biological sample components. The resolutionof these two mixtures indicates that the device and methods describedherein can be used to distinguish among various free-living and sessileforms of microorganisms.

The Raman spectral data shown in FIG. 7B indicate that the fiveorganisms shown have image spectra differ from one another, at least inminor ways. These signals were observed in organisms in cropped portionsof the entire field of view. These data indicate that, in addition todifferentiating between sessile and planktonic forms of a single speciesof microorganism, the methods and devices described herein can be usedto distinguish less dramatic variations among individual organisms of asingle species. In view of known functional differences among the cellsof a single biofilm, these results suggest that the methods describedherein can be used to identify and assess the role(s) performed byorganisms within a biofilm.

Numerous Raman- and other spectral-enhancing substrates are known in theart. The substrate used in the experiments described herein was a simplealuminum-coated microscope slide. It is contemplated that use of aRaman-enhancing substrate (e.g., one composed of a gold surface or goldmicroparticles) can further enhance the differences seen herein betweensessile and planktonic organisms and can also be used to detect andcharacterize more-subtle differences among organisms. Suitablesubstrates are described in the art, often in association with the term“surface-enhanced Raman spectroscopy” or “SERS.”

The disclosure of every patent, patent application, and publicationcited herein is hereby incorporated herein by reference in its entirety.

While this subject matter has been disclosed with reference to specificembodiments, it is apparent that other embodiments and variations can bedevised by others skilled in the art without departing from the truespirit and scope of the subject matter described herein. The appendedclaims include all such embodiments and equivalent variations.

1. A method comprising: depositing a sample containing a samplemicroorganism onto a substrate; irradiating said sample containing saidsample microorganism with substantially monochromatic radiation;obtaining a Raman data set based on radiation scattered from saidirradiated sample; first searching a database in accordance with theRaman data set in order to identify a first known Raman data set fromsaid database, wherein said database contains a plurality of first knownRaman data sets, and wherein each first known Raman data set isassociated with a known sessile form of a corresponding knownmicroorganism; and identifying a sessile form of the samplemicroorganism based on the first known Raman data set identified by saidfirst searching.
 2. The method of claim 1 wherein said database furtherincludes a plurality of second known Raman data sets, wherein eachsecond known Raman data set is associated with a known planktonic formof a corresponding known microorganism, and each second known Raman dataset has a corresponding first known Raman data set, further comprising:second searching said database in accordance with the Raman data set inorder to identify a second known Raman data set from the database; andidentifying a planktonic form of the sample microorganism based on thesecond known Raman data set identified by said second searching.
 3. Themethod of claim 1, wherein said plurality of first known Raman data setsincludes one or more of the following: a plurality of Raman spectracorresponding to the known sessile forms of the respective knownmicroorganisms, and a plurality of spatially accurate wavelengthresolved Raman spectroscopic images corresponding to the known sessileforms of the respective known microorganisms.
 4. The method of claim 2,wherein said plurality of second known Raman data sets includes one ormore of the following: a plurality of Raman spectra corresponding to theknown planktonic forms of the respective known microorganisms, and aplurality of spatially accurate wavelength resolved Raman spectroscopicimages corresponding to the known planktonic forms of the respectiveknown microorganisms.
 5. The method of claim 1, further comprisingobtaining a spectroscopic data set from multiple locations of the samplemicroorganism.
 6. The method of claim 5, wherein the spectroscopic dataset represents a spectroscopic property of the multiple locations of thesample microorganism, said spectroscopic property selected from thegroup consisting of: reflectance, absorption, transmission,fluorescence, polarization, and combinations thereof.
 7. The method ofclaim 1, wherein said sample further includes extracellular materialassociated with the sessile form of the sample microorganism, whereinsaid Raman data set includes a data subset associated with theextracellular material, and wherein said database further includes aplurality of second known Raman data sets, wherein each second knownRaman data set is associated with a corresponding known extracellularmaterial, further comprising: second searching said database inaccordance with the data subset in order to identify a second knownRaman data set from said database; and identifying the extracellularmaterial associated with the sessile form of the microorganism based onthe second known Raman data set identified by said second searching. 8.The method of claim 1, wherein said sample comprises a tissue of ananimal having a pathogenic condition at a localized region.
 9. Themethod of claim 8, wherein the animal is a human and the localizedregion is an ear of the human and the pathogenic condition is an earinfection.
 10. The method of claim 1, wherein said sample comprises atissue of an animal having a pathogenic condition and said animal havinga region that may contain a reservoir of the sample microorganism. 11.The method of claim 1, wherein said sample is a tissue of an animalhaving a septic condition.
 12. The method of claim 1, wherein saidsample includes cells associated with the sessile form of the samplemicroorganism, wherein said Raman data set includes a data subsetassociated with the cells, and wherein said database further includes aplurality of second known Raman data sets, wherein each second knownRaman data set is associated with corresponding known cells having anassociated function.
 13. The method of claim 12, further comprising:second searching said database in accordance with the data subsetassociated with the cells to identify a second known Raman data set; andidentifying the cells associated with the sessile form of the samplemicroorganism based on the second known Raman data set identified bysaid second searching.
 14. The method of claim 13, further comprising:treating the cells associated with the sessile form of the samplemicroorganism with a compound, thereby providing a treated sample;irradiating the treated sample with said substantially monochromaticradiation; obtaining a second Raman data set produced by the irradiatedtreated sample; comparing the Raman data set with the second Raman dataset; and determining an effect of the compound on the cells associatedwith the sessile form of the sample microorganism based on saidcomparison.
 15. The method of claim 14, wherein said compound includesan antibiotic.
 16. The method of claim 1, wherein said sample is asurface site.
 17. The method of claim 16, further comprising: contactingthe surface site with a probe in order to obtain the Raman data setproduced by said irradiated sample.
 18. A system comprising: a substrateupon which a sample is deposited; an irradiation source; a spectroscopicdevice; a database having a plurality of known Raman data sets, whereineach known Raman data set is associated with a known sessile form of acorresponding known microorganism; a machine readable program codecontaining executable program instructions; and a processor operativelycoupled to said irradiation source, said spectroscopic device andconfigured to execute said machine readable program code so as toperform the following: configure said irradiation source to irradiate asample containing a sample microorganism with substantiallymonochromatic radiation; configure said spectroscopic device to obtain aRaman data set based on radiation scattered from said irradiated sample;search said database in accordance with the Raman data set in order toidentify a known Raman data set from said database; and identify asessile form of the sample microorganism based on the known Raman dataset identified by said searching.
 19. The method of claim 1 wherein saidsubstrate comprises at least one of the following: a replaceablesubstrate and a resident in situ substrate.
 20. A method comprising:depositing a sample containing a sample microorganism onto a substrate;irradiating said sample containing said sample microorganism withsubstantially monochromatic radiation; obtaining a Raman data set basedon radiation scattered from said irradiated sample; processing saidRaman data set using a method selected from the group consisting of:principal component analysis (PCA), multivariate curve resolution (MCR),cosine correlation analysis (CCA), Euclidian distance analysis (EDA),partial least squares regression (PLSR), or spectral mixture resolution(SMR); first searching a database in accordance with the Raman data setin order to identify a first known Raman data set from said database,wherein said database contains a plurality of first known Raman datasets, and wherein each first known Raman data set is associated with aknown sessile form of a corresponding known microorganism; andidentifying a sessile form of the sample microorganism based on thefirst known Raman data set identified by said first searching.
 21. Amethod comprising: depositing a sample containing a sample microorganismonto a substrate; irradiating said sample containing said samplemicroorganism with substantially monochromatic light; obtaining at leastone Raman data set based on radiation scattered from a plurality ofspatial locations of said irradiated sample; combining at least one ofsaid Raman data sets with a visible light reflectance image of saidsample to thereby determine at least one of the following: the presenceof a sessile form of a microorganism in said sample, the absence of asessile form of a microorganism in said sample, the presence of aplanktonic form of a microorganism in said sample, the absence of aplanktonic form of a microorganism in said sample.
 22. The method ofclaim 21 wherein said substrate contains at least one of the following:a replaceable substrate and a resident in situ substrate.
 23. The methodof claim 21 further comprising processing said Raman data set using amethod selected from the group consisting of: principle componentanalysis (PCA), multivariate curve resolution (MCR), cosine correlationanalysis (CCA), Euclidian distance analysis (EDA), partial least squaresregression (PLSR), or spectral mixture resolution (SMR).
 24. The methodof claim 21 wherein at least one of said Raman data sets comprises aRaman chemical image.
 25. The method of claim 21, wherein said samplefurther includes extracellular material associated with the sessile formof the sample microorganism, wherein said Raman data set includes a datasubset associated with the extracellular material, and wherein saiddatabase further includes a plurality of second known Raman data sets,wherein each second known Raman data set is associated with acorresponding known extracellular material, further comprising: secondsearching said database in accordance with the data subset in order toidentify a second known Raman data set from said database; andidentifying the extracellular material associated with the sessile formof the microorganism based on the second known Raman data set identifiedby said second searching.